Glossary of terms
Data engineering is a set of operations to make data accessible and useable to data scientists, data analysts, business intelligence (BI) developers, and other organizational professionals. It requires specialists to both design and build systems to collect and store massive volumes of data on a wide scale, and to prepare it for further analysis. The process of data engineering consists of accomplishing a sequence of tasks that transform large amounts of raw data into a meaningful product that meets the needs of analysts, data scientists, machine learning engineers, and others.
Simplified structure of the data engineering process: When data is consumed (Data ingestion), it is transferred from many different sources such as SQL and NoSQL databases, IoT devices, websites, streaming services, and so on to the target system to be transformed for further analysis. Raw data comes in various forms and can be either structured or unstructured. In Data transformation, the dissociated data is customized to meet the needs of end users.
This step involves eliminating errors and duplicate data, normalizing it, and converting it into the desired format. Data serving delivers the transformed data to the end users – BI platform, dashboard, or data science team. Data flow orchestration provides visibility into the data engineering process, ensuring that all tasks are completed successfully. It coordinates and continuously monitors data processes to identify and correct data quality and accuracy issues. The mechanism that automates the consumption, conversion, and transmission stages of the data engineering process is called a data pipeline. Data Engineers can work in a wide variety of fields: finance, tourism, advertising, security, and e-commerce. Simply put, they work on a project or product that requires working with large volumes of data, speed, or variety in structure and format.
In today’s fast-paced digital landscape, businesses are increasingly moving towards cloud automation to streamline their operations and stay competitive. As companies scale and adapt to
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